Technical articles on AI agent memory, context engineering, and building with Harbor.
Agent frameworks focus on tool orchestration. But the real bottleneck is context management — what enters the context window, how conversation history accumulates, and why relevant context beats raw data every time.
When an LLM generates a response, it samples from a probability distribution shaped by context. External memory doesn't help the model 'remember' — it shifts the probability distribution so that informed tokens become the most likely next output. This is context engineering at the token level.
Softmax attention distributes a fixed probability budget across all tokens. Every irrelevant token in the context window steals attention from relevant ones. The math explains why 47 fields in and 3 fields out isn't about saving tokens — it's about attention quality.
AI agents are stateless by design. Every forward pass through a transformer is independent — there is no persistent state between sessions. This isn't a bug. It's an architectural inevitability that demands an engineering solution.